Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2014 -2015

C O N T E N TS
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■ History—

Message from Director-General

The Institute of Statistical Mathematics (ISM) was established in 1944,
close to the end of World War II. This year, therefore, marks the 70th anniversary of our founding. The ISM was launched with the aim of conducting
studies on the mathematics of probability and its applications, as well as ofcommunicating, unifying, and promoting such studies. To this day, without
interruption, the same determined spirit of inquiry that was directed to research in those early years has been preserved in our identity, enabling us
to make contributions to expanding the depth and advancing the frontiersof statistical mathematics. With two years remaining of our 2 nd Mediumterm Plan as an Inter-university Research Institute Corporation, we must
face the challenging environment surrounding national universities andalso this year accelerate our preparations for transitioning to the 3rd term.
This 2014-2015 Japanese academic year will be the final year of my (first
four-year) term as Director-General. I was appointed amid the confusion that
followed the Great East Japan Earthquake — the youngest person ever totake on the job — and to the best of my abilities, I strived to get our two major
projects at the institute, the Network Of Excellence (NOE) Project and the Project for Fostering and Promoting
Statistical Thinking, fully up and running and to ensure the stable operation of the institute.
The three key focal points of operation in this academic year are (1) advancing our inter-university and cooperative research functions, (2) enhancing our organizational capacity, and (3) formulating a first draft for our 3rd
Medium-term Plan.
Within the first half of this academic year, we have introduced three supercomputer systems, each equippedwith different functions, in part with the help of supplementary budget funds. One of these supercomputers is tobe part of Japan’s revolutionary High-Performance Computing Infrastructure (HPCI), a key component of Japan’snational computing infrastructure. ISM is the first Inter-university Research Institute to join HPCI. We are intent on
systematically utilizing the three systems, in accordance with the diverse skills and purposes of users, and to efficiently promote their use for inter-university and cooperative research. In addition, we plan to start construction
on an extension of our guesthouse (to increase the number of apartments), which has proved so fruitful in helping
us to host prominent researchers from abroad.
To enhance our organizational capacity, with budget funds from the program for promoting the enhancementof research universities by the Ministry of Education, Culture, Sports, Science and Technology, we are investing
effort in ensuring the stable operation of a group of University Research Administrators (known as URAs) appointed in November of last year to function as support enhancing research at ISM. We will conduct a drastic overhaulof our public relations system, which has been inadequate. Utilizing this URA human resource team, we will aim
for the realization of public relations activities that are more efficient and of higher quality. We recognize that withall the current public attention on “big data” and statistical science the role of public relations is extremely vital.
Although statistical mathematics is difficult to understand and unapproachable to most people, because our research plays an important role in society, we are committed to communicating and explaining what we do in an
approachable way.
In April of this year, a large commercial facility was built on vacant land close to ISM and within about one anda half years, another large commercial facility is expected to be constructed nearby. Clearly, this is one of the fastest growing parts of the western half of Tokyo. With the influx of many new restaurants, the variety of meals isgreatly expanding, making life much more convenient for visitors staying at our guesthouse. ISM can take advantage of this trend to strengthen itself as an Inter-university Research Institute, thereby making our contribution to
the development of the local economy.
As a research center focused on data-centric science, in the face of fierce global competition, we remain dedicated to meeting the expectations of society through the coordinated efforts of all our personnel. We look forward
to your continued understanding and support in this mission.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of
the modeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, thedepartment aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, which function effectively interms of predicting phenomena or scientific discoveries, through data analysis and modeling related to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Data Design Group ——————————————————————————————————————————————————The Data Design Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of survey and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms in statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain phenomena,
stochastic processes and their application to inference, probability and distribution theory, and the related mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis, optimization,
discrete mathematics, and control and systems theory for computation-based statistical inference as well as their applications.

NOE-type Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the uncertainty and risks in society,which have increased with the growing globalization of society and the economy. The Center is also constructing
a network for risk analysis with the goal of contributing to creating a safe and resilient society.
2Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to produce designs for effective observation systems by means of “data assimilation”, which is afundamental technology integrating numerical simulations and observational data.

Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanning overhalf a century including the Study of the Japanese National Character and the cross-national comparative researchon national characteristics, the Survey Science Center was established in January of 2011 in order to facilitate
further growth of the aforementioned sets of research as well as the establishment of networking ties with both
domestic and international research organizations and the increase in the capacity to make contributions to wider
society by creating what we call the NOE (Network Of Excellence).

Research Center for Statistical Machine Learning
Machine learning is a research field studying autonomous systems that learn from data. This field is based on
the statistical science that concerns inference from data and the computer science that studies algorithms. The
application of machine learning is broad, ranging from engineering, including robotics, and information sciences
to natural science, such as brain science. This research center aims at supporting the academic community of the
field, in addition to producing influential works via various joint projects.

Service Science Research Center
The aim of the newly established Service Science Research Center is to apply data-centric methodologies tothe service fields — from marketing, supply chain management, and management engineering, to the modelingof social systems. We will integrate the insights from these fields to establish data-centric service sciences as acommon discipline, through collaborations with universities and institutions worldwide, taking advantage of ISM’s
Network Of Excellence (NOE) program.

Professional Development
School of Statistical Thinking
The School of Statistical Thinking was established as a center for the planning and implementation of various
programs for professional development and education and training in statistical thinking. In the setting of a joint
research facility, the school is working to develop professionals (specialists with broad knowledge and skills,
modelers, research coordinators, etc.) equipped with the statistical thinking ability to meet the demands of the “bigdata era”, in which large-scale data sets are utilized for modeling, research coordination, and other applications.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, software for research, networking infrastructureand network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining an extensive library and an electronic repository, and is incharge of planning statistical education courses to popularize research results.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Research Centers
Risk Analysis Research Center

Risk Management Based on Extreme Value Theory
N Extreme value theory and its applications

N Evaluating the encounter probability to natural

A massive earthquake rarely occurs. Neverthelessit causes great damage on society.
Extraordinary natural phenomena such as torrential
rain, tremendous snow and strong wind cause natural
disaster such as flood high tide, tsunami and etc. Not
only in natural disasters, in general, it is no exaggeration
to say that the most important in risk management is
to tackle rare but serious events. Extreme value theory (EVT) deals with extraordinary events not ordinary
ones. A cooperative research symposium “Extreme
value theory and its applications” has been held since
1994 and its report is published and distributed widely.

hazards by use of the historical extremes
Precipitation, discharge in river, temperature, windspeed, sea level, wave height at offshore; extremes ofthese natural forces are very important factors to examine the risks which give a great menace to the infrastructures (electric power, water supply, gas, transportations) and public works for support to our daily
life. The Netherlands, almost 40% of which is beneaththe mean sea level, suffered the devastating damage
due to the North sea flood 1953. It has accomplishedthe coastal defenses, so-called Delta plan, for the protections of the lowest areas. In this plan, EVT has been
incorporated to determine the design of coastal dikes.
After the 2011 Tohoku earthquake and tsunami, the older historical data excavated in strata and in old documents have recently attracted the public interest in the
viewpoint of a clue to the occurrence probability of huge
force. One of our projects is to develop new methodsof evaluating probability of the natural hazards and to
make effective use of the older and ambiguous historical data when attached in the modern observations.

Figure 1: The invited talk by Dr. Fujibe ( 2013 ).

N Spreading application fields and developing theory

In addition to the prevention to natural disasters,
EVT has been applying to various fields in insurance
and reliability. Moreover, EVT is getting to spread its
application to biometrics, sports statistics. Risk management in finance is one of them. Black Monday andBankruptcy of Lehman Brothers have a great impact
on world economy. As these examples show, recently,
extremely large fluctuations in finance give massive
damage on worldwide scale. In parallel with spreading
application areas, statistical theory is also developing
to analyze complicated real phenomena.
Takaaki Shimura

Figure 2:
The worst 10 floods sea level in the historical term of
350 years, adding to the annual maximum in the modern observation of 108 years, at the Hoek van Holland in
the Netherlands. In the figure the red point is the valueof the North sea flood of 1953, and the contour linesand gray density plot shows the degree of experience,
which indicates the estimation error of the return levels.
[ by T. Kitano (Nagoya Institute of Technology )]4

Figure 3:
QQ plot applying generalized extreme value distribution to monthly minimumof price-earnings ratio ( TO­
PIX 1990-2010 ).
[ by N. Makimoto ( Tsukuba
Univ.)]

Exploring Efficient and Effective Management Approaches
to Mitigate Risk by Disturbance
N Optimization model for forest management

planning“When, where and how much to harvest” has been a
primary concern for resource managers for many years.
In order to assist such decisions, considerable effort
has been given to develop optimization models searching for the optimal management strategy, which maximizes the amount of harvest from resources or profit
induced from such harvests subject to given constraints.
Integer programming allows us to formulate the constraints necessary to avoid resource degradation due
to unplanned harvesting activities. For example, harvested units can be dispersed over space and time to
avoid the creation of large openings, simply by introducing adjacent constraints that prohibit harvesting
adjacent management units in the same period.

on the spatial information of each management unit.
Not only can GIS be use to archive spatial information,
but it can also be used efficiently to generate adjacency relationships among management units. GIS can beused to visualize the optimal spatial harvest patterngenerated from an optimization model (Fig.2).
N Mitigating the risk surrounding forest resources

Figure 1: Adjacency relationships among management units.

with optimization model
As a result of global climate change, forest managers
are now faced with the challenge of how to mitigate thedamage caused by disturbance agents such as forest
fires, insects attack, and invasive species. Damages
caused by disturbance agents are spatial in nature, forexample, fire spreads from an ignition location overspace and time. Spatial patterns of forest vegetation
affect the degree and extent of forest damages causedby these disturbance agents. Fire for example, spreads
rapidly when it enters a contiguous area where fuelhas been accumulated. Forest managers can mitigate
the damage caused by disturbance agents simply bycontrolling the spatial distribution of forest vegetation
which takes into account the expected spatial damage
patterns. Optimization models can be used to explore
the optimal spatial patterns that mitigate the damages
caused by disturbance agents. The results presented
in Fig 3 were produced by integrating a simulation model that projects spatial damage patterns into an optimization model to search for the efficient and effective
forest management allocation patterns for mitigatingthese kinds of risk.
Atsushi Yoshimoto

Figure 2: Visualizing optimal spatial harvest pattern with GIS.

Figure 3: The optimal management pattern for mitigating
the risk of damage from invasive species.

N Use of Geographic Information System (GIS )In order to consider spatial distribution of resource
harvest units, it is important to figure out adjacency
relationships (Fig. 1) among management units based5Research and Development Center for Data Assimilation

Participation in the Program for Risk Information on Climate Change
N Purpose of this program

Our group has been an associated member of theProgram for Risk Information on Climate Change
since 2012. The program aims to generate basic information required for managing various risks resulting
from climate change using Global Climate Models(GCMs) and Regional Climate Models (RCMs) withdownscaling techniques. We have two goals. First
one is to estimate probability distribution of “climate
reproductions” by the various climate models andto develop an evaluation of “predictions of future climate” for each climate model. The other is to generate a probability distribution of extreme events suchas typhoon attacks to a big city like Tokyo. Focusing
on the latter, we introduce our research here.
N Necessity of a stochastic tropical cyclone model

Recent studies have pointed that there are someproblems in GCMs about Tropical cyclones (TCs)predictions. Basically, the number of TCs was notlarge enough to obtain their probability distribution. The estimated number of TCs which may occur
around the East Asia is around thirty per year. The
calculation of the GCM simulation needs a long timewith a huge numbers of CPUs on super computers.
For these reasons, we cannot gain sufficient TC tracks
from GCM and RCM outputs to estimate the probability distributions. Moreover, it is known that there
are biases on the GCMs and RCMs.

Figure 1: A flowchart of ISM-stochastic TC model.

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To solve the problem of the insufficient TC numbers, we are developing a stochastic TC model thatgenerates a lot of TC tracks. Some models have beendeveloped to solve the same problem, insufficientTC numbers. However, researchers found that there
are biases in the stochastic models themselves. For
keeping out of this problem for both of biases in
GCMs and stochastic models, our stochastic TC model is applied to JMA/MRI-AGCM (developed by Meteorological Research Institute, Japan Meteorological
Agency) outputs.
N ISM-stochastic tropical cyclone model

The ISM-stochastic TC model has three components based on a typhoon life cycle: generation, transition and dissipation. Figure 1 indicates a flowchartof the model. Artificial TC tracks in September were
calculated using this model (Fig. 2). We are going tostep up our research to estimate probability distribution of TC tracks in the future climate calculated
by GCMs / RCMs.
Kazue Suzuki, Shin’ya Nakano,
Yukito Iba and Genta Ueno

Figure 2: Tracks of the artificial TCs in September calculated by
the ISM-stochastic TC model. Locations of the generation are selected by the bootstrap method based on
the observation data ( JMA, 1951- ).

Research Centers

Comprehensive Understanding of the Nervous System by Data Assimilation
N Live-cell imaging of nervous activity

The nervous system of animal living organisms realizes advanced and robust information processing in
response to diverse external signals. However, there
are many missing pieces of the puzzle about in the principle of action in the neuronal circuit system. Recently,
a rapid advance of live-cell imaging techniques for nervous activities has been made it possible to visualize afine texture of nervous activities at the molecular andcellular levels. In neuroscience, a model organism called
C. elegans has been investigated well. The neural circuitof C. elegans consists of only 302 neurons and nearly
7,000 synapses, and both of them are completely identified. I am now engaged on a collaborative project thataims to unveil the whole nervous system of C. elegans
in the living state using the calcium ion imaging method. In the project, temporal changes of the nervous
activities are measured by a confocal microscope andrecorded as a time-lapse 3D image sequences. By analyzing the live-cell images, we aim to clarify the principleof action of the complex dynamic system.
N Data assimilation on the neural circuit modelof C. elegans
In the present study, a simultaneous observation of
150 neurons is on the verge of becoming possible. Besides, a simulation model of the whole nervous system
is being developed based on the databases of synaptic
connectivity of C. elegans. Using data assimilation methods, we aim to estimate internal states of the whole nervous system by integrating the simulation model andthe records of neuronal activities partially observedby the calcium ion imaging. Figure 1 illustrates a roadmap for our project. The project team consists of three

Figure 1: Road map of the project.

groups: the experiment group, the modeling groupand the data analysis group. The data analysis groupis led by Associate Professor Yoshida at ISM. Our groupplays a role of integrating the live-imaging data provided by the experiment group and the neural circuit
model developed by the modeling group. Data assimilation techniques are used to identify a parameter setof the simulation model. In addition, the data assimilation will be a powerful tool for the reconstruction of
the internal states of the whole nervous system including activities of unobserved neurons.
N Image processing method for automaticquantification of neuronal activities
In order to improve throughput of the project, weare building a pipeline of image processing methods
that automates all the quantification process based ontime-lapse 3D images. The currently obtained 3D image
data contains approximately 120 neurons. During themeasurement, the worm moves irregularly. In order toquantify the activity of each neuron, the trajectory ofneurons must be tracked exactly. However, an exact
tracking of cells located around head is quite difficult
because they are distributed densely and sometimes
move rapidly. Base on an idea of Bayesian statistics,
we developed the new tracking algorithm that can
track such many cells with a high degree of accuracy
even where cells are distributed densely (the result isshown in Figure 2). Although many tracking methods
have been developed in the field of computer vision,
any of them could track such densely distributed moving objects. We expect that our tracking technique isapplicable for not only neuroscience but also othervarious problems.
Terumasa Tokunaga

Figure 2: Tracking of many neuronal cells.

7Survey Science Center

The Asia-Pacific Values Survey— A Study on Cultural Manifold Analysis ( CULMAN ) —N From “Statistics for War” to “Statistics for Peace”

Based on a proposal submitted by Japan National
Research Council, the Institute of Statistical Mathematics (ISM) was founded under the Ministry of Educationin 1944 during WWII. After the war, the ISM’s mission
was shifted from operations research to the development of official statistics and scientific public opinion
systems for the post-war democracy in Japan. Most of
systems on official statistics and survey research were
established by the ISM as various collaborated works
with members of other universities, institutes and thegovernment, i.e., all over Japan.
N Japanese literacy survey & democracy

During the post-war time, some people of the US
government insisted on “Romanization of the Japanese
language”, because they believed that the use of Chinesecharacters presented difficulties in education and daily life. In 1948, the ISM played a central role in an interdisciplinary team under the Ministry of Education inorder to carry out a nationwide survey on “the Japanese

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Literacy” based on statistical random sampling theory.
The result confirmed the sufficient literacy of the Japanese to develop the post-war democracy and it eventually saved the Japanese language from possible social
confusions. Based on the methodology invented in thesurvey, the ISM has started “the Japanese National
Character Survey” in 1953 to study the attitudes, opinions, and social values of the people in daily life, whichlasts over 60 years now. This is a world- widely uniqueand significant survey supported by the Ministry of
Education, and it stimulated many other countries tostart the same type of statistical sampling survey suchas GSS in the USA, EuroBarometer in EU, etc.
N Cross-national comparative survey

Figure 1: Mt. Fuji, Japan.

on national character
Since 1971, it has been extended to cross-national
surveys for more comprehensive study, including surveys on the Japanese immigrants in Hawaii and Brazil.
The most significant task involves the cross-national
comparability of the peoples beyond differences suchas cultures, languages, and social systems. We havedeveloped a paradigm called “Cultural Linkage Analysis” to overcome the difficulties of comparability,
which has now evolved as “Cultural Manifold Analysis(CULMAN)” for the three sets of hierarchical comparative frameworks: time, space, and research themes.
Now the Asia-Pacific Values Survey” has been in progress with the support of Japan Society for the Promotion of Science during 2010-2014 fiscal years.
• http://www.ism.ac.jp/ism_info_j/kokuminsei.html• https://www.jsps.go.jp/j-grantsinaid/12_kiban/
ichiran_22/e-data/e33_yoshino.pdf
Ryozo Yoshino

Figure 2: Hanoi, Vietnam.

Figure 3: New Delhi, India.

Research Centers

Multi-level Approaches in the Japanese National Character
and Organizational Climate
N The social system and the Japanese National

Character
The Institute of Statistical Mathematics has been
conducting a longitudinal nationwide survey, called“Nihonjin no Kokuminsei Chosa” on the Japanesenational character since 1953. The main goal of thesurvey is to investigate nature of Japanese people
and track change of Japanese society. I focus onorganizations and groups to whom an individual
belongs as part of national character research. The
atmosphere of workplace are mentioned as an example.
In order to assess the most recent attitudes andbehaviors of the Japanese National Character in organizational climate, it is important to examine individuals embedded within organizations or countries.
To achieve this goal, on top of individual assessment,
we also focus on the variables in an organizationlevel for each organization, such as corporate, school,
regional society, etc. that an individual belongs to.
Furthermore, we are conducting cross-cultural research on the national character including international comparison, so as to catch the Japanese National Character in the wide range and various contexts.
N Large-scale surveys and new statistics

techniques
In order to develop a study of the Japanese National Character, it is required to conduct large-scale
surveys based on a statistical sampling method, andcarry out surveys on Japanese National Character
including international as well as inter-organizationcomparative study by using the new statistical techniques such as structural equation modeling andmultilevel analysis.
Yoosung Park

Figure 1:
The mechanism of the intrinsic motivation and teamwork in an individuallevel.

Figure 2: Moderating effect of nation and organizationin the Japanese National Character.

Figure 3: The machanism of the interpersonal facilitation in multilevel.

9Research Center for Statistical Machine Learning

Research on Adaptive and Learning Control Theory
N Research on modeling and control

N Research on coordinate control

of complicated systems
The purpose of the research project is to obtain
integrated design procedures of modeling and control for a class of complicated systems having nonlinear characteristics and high-order vibrating modes
such as nonlinear mechanical processes, large flexible structures and flexible arms. Linear and nonlinear parametric models are introduced to identify uncertain processes, and adaptive H∞ control schemes
are developed based on inverse optimality in order
to attenuate the effects of modeling errors and neglected high-order modes. Adaptive inverse models
are also introduced to compensate uncertain input
nonlinearities such as dead-zones and backlashes.
Sophisticated integrations of modeling and control
are highly required in order to achieve the research
objective.

of multi-agent systems
In the research project, coordinate control problems of multi-agent systems, such as formation control and consensus control for plural components are
considered, and fundamental control principles oflarge-scaled complex systems, such as smart highway and coordinate control of multiple manipulators or space crafts, are to be developed. The limited
communication structure among agents is also an
important topic in the control problem, and it is described as an information network graph. Adaptive
H∞ control schemes are introduced in order to attenuate the effects of errors in system models, related
potential functions or imperfect information of thevirtual leaders. The compatibility of modeling andcontrol of uncertain multi-agent systems is an indispensable factor of the research project.
Yoshihiko Miyasato

Figure 1: Reduced-order disturbance attenuation control of highorder vibration processes ( distributed parameter systems) and input compensation.
Figure 3: Formation control of multi-agent systems.

Figure 2: Identification and control of nonlinear parametric model.

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Figure 4: Information structure ( network graph ) ofmulti-agent system.

Research Centers

Creating Efficient Index to Assess Marine Biodiversity
via Machine Learning
N Prediction for global collapse for fish stocks

in 2048
A project of CREST, JST on developments for ecological assessments with partial data via fusion ofmarine ecology and machine learning started in2012 with team leader, Hiroshi Okamura at National
Research Institute of Fisheries Science. In the community of marine ecology there are different ideas
about the sustainability for fish stocks. In particular
Worm et al. published a shocking conjecture in Science that all fish stocks will collapse by 2048. At
that moment New York Times featured this paper at
the top page, and all mass media also reported thisnews. On the other hand several papers pointed
from a viewpoint of fisheries science that this prediction is misleading. In this way there occurred

Figure 1: (Top) raster plot of spike trains.
(Bottom) an estimated firing rate.

Figure 1: Relative catch curves in fish groups.

confused situations with different opinions for theassessments, so that a problem of strong limitation
for data release was faced for scientists. Quite recently a tendency for data release pushes us to join thisproject as statistical researchers.
N RAM Legacy Fish Stock database

Worm et al. define collapse for a fish stock by acondition that the catch at the year is less than 10%of the maximum catch. However there is an essential
question of “Does catch reflect abundance?” Thesolution for this question is a key to clarify the problem of which prediction for fish stocks is correct. As
one of difficult aspects to solve this there are backgrounds for data such that any biomass data are
missing and only catch data are available. For example FAO has global records for fish dataover 50 years however the records consist of fish name, region code and catch
without any biomass assessments. We
approach a statistical assessment for
RAM Legacy Fish Stock database which
is dedicated by Ransom A. Mayer. The
biomass variable is reduced to a binary
label whether the relative biomass isless than 20% or not. Hence the objectivity of the analysis is to predict the binary labels of collapse and non-collapse
from catches, trophic levels, region codes
and maximum lengths as feature variables. We propose asymmetric logistic
model which permits the different models for the collapsed and non-collapsed
samples. As a result the analysis in theasymmetric model has much better performance than the conventional catchonly-method used in Worm et al. We
think that if the statistical model appropriately combines not only catches but
other variables with mixed effects, thenwe can build a good prediction method.
We will employ this proposed method
to predict the fish stock status in FAO
data as the next project.
Shinto Eguchi

Figure 2: Prediction for fish stock status.

11Service Science Research Center

Prediction of Purchasing Behavior by Age GroupAn important issue in a super-aging society is deciding on a policy under which elderly people can be
actively involved in the society’s production and consumption processes. To realize such a policy, technology for predicting future purchasing behaviors of elderly people is crucial. We investigated the predictionof consumer awareness and of changes in purchasing
behavior due to awareness by using two datasets, consumer purchase history data and media-integration
research data.

the contact situation with the primary medium (television, radio, magazine, or Internet). They also include
survey data that captures the situation (product use,
possession, or purchase) for the same specimen set.
Moreover they include research data related to consumer awareness and behavior. The data cover about
20 years, so the long-term trends can be understoodin terms of purchasing behavior by consumer generation, consumer age, and time period.

N Consumer purchase history data

The consumer purchase history data and the mediaintegration research data commonly include behavior
place and time, environment, health, and interest in
the media. Here we utilize these common items. Consumers represented in the media-integration researchdata were grouped in accordance with their age andgeneration. Each group was characterized by the responses for the common items. For each item, we examined the ability to predict consumer awareness inthe future by using multiple regression analysis withthe response as a dependent variable and the consumer’s age and survey year as independent variables.
Figure 1 shows the results for “has a strong interest in
health” for each age group from 2013 to 2020.

The consumer purchase history data was productpurchase survey data obtained using a bar code scanner. While its use enabled individual product purchases to be exhaustively tracked, the survey has been
underway for only a few years, so tracking long-termchanges in purchasing behavior was difficult. However, the dataset includes an abundant amount ofshort-term information on purchasing behavior. Since
consumer attitudes were surveyed as well, the relationship between purchasing behavior and consumerawareness could be clarified.
N Media-integration research data

N Prediction of consumer awareness in the future

The media-integration research data include questionnaire survey data that capture, for a specimen set,

N Prediction of changes in

Survey years
Survey years

purchasing behavior in the future

Ages
Ages

Years
Years

Figure 1: Prediction rates [%] for “has a strong interest in health” for each age
group from 2013 to 2020.

Ages
Ages

Figure 2: Purchase amount (per month) [yen] estimated for each age group.

12

Using the prediction results (that is,
the predicted responses for the common items in the future) based on the
media-integration research data for consumer awareness, we examined the prediction of purchasing behavior in the
future by multiple regression analysisof the responses for each group (classified by purchase behavior) in the consumer purchase history data with thecommon items as independent variables and the purchasing behaviors as
dependent variables. Figure 2 shows
the results of estimating the purchaseamount (per month) for each age group.
We examined the prediction of changes in purchasing behavior by integrating
short-term purchase history data withlong-term media-integration researchdata.
Tomoko Matsui andHiroshi Maruyama

Research Centers

How to Develop Data Analytics Talents
N Developing talents for big data

Data analytics is quickly becoming an essential
tool to make business decisions in various industries, such as retail, distribution, medical, education, and public sectors. One of the major obstacles
of applying data analytics is the lack of talents. To
address this issue, The Ministry of Education, Culture, Sports, Science & Technology in Japan (MEXT)
launched a three-year project for developing data
analytics talents and making the best use of them.
The ISM is leading this project with the help of The

Graduate School of Information Science and Technology, The University of Tokyo. The goal is to identify
the model of talent development and their careerthat are best suited in Japan, and to share the model
among the stakeholders such as academia, industry,
and the governments as well as NGO’s.
N Current status of “Data Scientists” in Japan

In the first year of the project, we conducted astudy on the current status of “data scientists” inJapan. The study consists of a) a survey on those who
passed certain levels of the certification tests of Japan Statistical Society
Certificate (319 valid responses) and b)
a series of interviews with 20 wellknown data scientists in Japan. The results indicate that typical data scientist in the United States and those inJapan are somewhat different; in theUS, data scientist are more productfocused, meaning that they develop
working systems with embedded data
analytics, while in Japan, data scientists are typically service professionals who help analyzing their clients’data.
N The next steps

Figure 1: The concept of the project.

Based on the findings acquired in
the first year of the project, we haveidentified skills needed in these data
scientists and started to develop program for them. The program includesa) an online training material, titled“Data Scientist Crush Course,” thatteaches basic data analytics concepts
in a very concise manner, and b) aninternship program that presents anopportunity for students who want tobe a data scientist to get first-hand experiences of analyzing data.
The detailed information on the
project can be found on our website at
http://datascientist.ism.ac.jp/.
Hiroshi Maruyama

Figure 2: Answers to “Why do you want to be a data analytics professional?”.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Biaxial Structure
The Institute of Statistical Mathematics pursues research and education along the two lines of basic research and NOE (Network Of Excellence)-type research,(we explain about “NOE” later), conducted by the basic
research departments along a horizontal axis and theNOE-type research centers along a vertical axis (Figure1). By its nature, the basic research departments cut
across and link various disciplines, developing tools forinterdisciplinary research. The field of statistical mathematics must itself evolve to meet the changing needs ofsociety and the data environment, and is therefore constantly evolving as a field of study. At the same time, there
are approaches and directions that remain unchanged
as the field evolves. For that reason, we have chosen not
to call it fundamental research or foundational research
but “basic research”, to reflect both the fixed and evolving qualities of statistical mathematics. There are threebasic research departments: Statistical Modeling, Data
Science, and Mathematical Analysis and Statistical Inference. These departments engage in cutting-edge research to develop methodologies for rational prediction
and decision making, based on data and existing knowledge. Each of the Institute’s permanent researchers is
assigned to one of these basic research departments.

N Goal-oriented Activities
The Institute’s goal-oriented activities are promoted by
NOE-type research centers and a school for developing
professionals. The NOE-type research centers (verticalaxis) are staffed by permanent researchers within theInstitute, project researchers (post-doctoral staff), andvisiting professors and researchers. The current NOEtype research centers are the Risk Analysis Research

Figure 1: Biaxial Structure for research and education

14

Center, Research and Development Center for Data Assimilation, Survey Science Center, Research Center for Statistical Machine Learning, and Service Science ResearchCenter. These centers conduct research activities thatinterface statistical mathematics with individual scientific disciplines in order to find solutions to urgent social
problems. In addition, these centers promote the NOE
activities as the cores in each NOE domain.
We launched the School of Statistical Thinking to foster and promote “statistical thinking”. In the School of
Statistical Thinking, researchers and students who havean eye on the creation of a new statistical research fieldand various people who realize the necessity of statistics in their particular research field train together tofoster statistical thinking. For that, we have changed
the personnel system to post newly employed assistantprofessors at the School of Statistical Thinking.

N NOE ProjectIn accordance with the second medium-term plan of
the Research Organization of Information and Systems,
the Institute of Statistical Mathematics has set as a goal
the establishment of NOE in statistical mathematics. We
have been establishing NOE in the five research areasof risk research, next-generation simulation, survey science, statistical machine learning, and service science.
The NOE Project pursues activities to establish new
methodologies in their respective research fields andserve as hubs for interdisciplinary interaction. We operate an NOE Promotion Unit to strengthen our coordination skills and to facilitate the overall advancement of
the NOE Project, crucially supporting the goal of erecting
a fourth scientific methodology in our knowledge society that extends beyond resolving individual problems.

Senior Advisor, RIKEN Brain Science
Institute

Dr. Shun-ichi Amari

Adjunct Professor, Computational Science
Education Center, Kobe University

Dr. Yoshio Oyanagi

President, Shiga University

Dr. Takamitsu Sawa

Professor Emeritus, Tokyo University

Dr. Motoyuki Suzuki

President, Center for International
Public Policy Studies

Prof. Naoki Tanaka

President, The Japan Pharmaceutical
Manufactures Association

Dr. Isao Teshirogi

Professor, Sociology and Research
Methodology School of Cultural andCreative Studies, Aoyama Gakuin University

Dr. Kazufumi Manabe

Director-General, Center for Research
Development and Strategy,
Japan Science and Technology Agency

Dr. Hiroyuki Yoshikawa

Director-General, Institute for
Monetary and Economic Studies

Prof. Tomoo Yoshida

Table 1: Advisory Board of the NOE Project ( April 1st, 2014)

The Advisory Board of the NOE Project, which is organized as experts in the private sector, academia, and thegovernment (Table 1), advises our Managing Committeeof the NOE Project and each NOE-type research centerso that we can formulate a unified management strategy
for the project.
On September 24 th, 2012, we held the Advisory Board
meeting of the NOE Project in order to explain our past
NOE activities and to collect opinions from the members of the Advisory Board for further research activities in the next stage of the NOE Project. (Photo 1)
Eight of nine members of the Advisory Board took
part in the meeting, and we could hear many valuable
opinions about the future of NOE. The logo of NOE (Figure 2) symbolizes the advancement of comprehensive
research through the combined and cumulative effortsof the five NOE domains. The logo is used for the activities of the NOE Project at the Institute and at our relatedorganizations.

N Expansion of Activities by NOE
The Institute is actively pursuing agreements withother research organizations in order to expand the roleof the five NOE-type research centers as core researchhubs. In addition, in the 2013-2014 Japanese academic
year, new previously agreed to Memorandum of Agreements between two NOE domains became effective.
From a methodological perspective, our Institute is

Figure 2: logo

Photo 1: Advisory Board meeting on Sept. 24th, 2012

focusing all activities on advancing research in each of
the research fields of risk research, next-generation simulation, survey science, statistical machine learning,and service science. Also, we plan to unite the five NOE
mentioned above with the NOE Research Promotion
Organization. Using this NOE Research Promotion Organization, the Institute will aim to develop new researchareas and establish new collaborative research systems(Figure 3). To learn more about the project, please visit
the website of NOE (http://noe.ism.ac.jp/english/).

Figure 3: Relationship Diagram of NOE ( Network Of Excellence )15

Project for Fostering and Promoting Statistical Thinking

Because of the “data explosion” in modern society, people who can think and handle such data statistically and retrieve useful information are greatly required in all fields of science and society. The
Japanese education system unfortunately has fallen short in producing a sufficient number of people
who have useful statistical thinking abilities. We established the School of Statistical Thinking in ISM to
improve this situation. We hope that our activities, including tutorial courses, seminars, and cooperative
research projects, are useful for developing professionals with the proper knowledge and skills to tackle
complicated problems involving huge amounts of data.

Research Collaboration Start-up
The Institute had been providing a consultation service on statistical science. Along with the launchof the School of Statistical Thinking in November 2011, this service was reorganized as a research collaboration start-up. This program, being one of the projects to foster and promote statistical thinking, ismainly aimed at supporting applied scientists and other related people. Expert statisticians affiliatedwith the Institute give scientists and others advice on statistical modeling, data analysis, and research.
Some cases develop into official research collaborations, which are our primary duty as an inter-university research institute. The start-up program covers a variety of topics, ranging from introductory tomore specialized studies. Half of the clients are from the private sector, and the rest are staff at publicorganizations, university professors, or students. The Institute accepts about 40 cases annually, some ofwhich benefit society in diverse ways.

Open-type Human Resource Development Program
In order to foster and promote “statistical thinking”, the Institute of Statistical Mathematics invites
proposals of research projects and workshops to develop young researchers. In FY 2014, one project andfive workshops have been adopted after review.

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars are led by in-house and external lecturers to
showcase their latest studies. These lectures are free
to attend. To view the seminar schedule and learn
more about the program, please visit the Institute of
Statistical Mathematics website.
http://www.ism.ac.jp/

16Open Lecture
We hold an open lecture during Education and Culture Week every year (November 1 through 7),
to introduce the Institute’s activities and to promote statistical science. We invite lecturers to speak
on a timely topic relating to statistical science. The lecture is open to the general public. For further
information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/

Tutorial Courses■ History
The Statistical Education Program started in 1944, the year that the Institute of Statistical Mathematics wasfounded, as an education program at the Numerical Computation Training Center of the Science Research Technical
Assistant Training Center of the Ministry of Education, located within the Institute. In 1947, the affiliated Statistical
Technician Training Center was opened as an educational organization for statistical technicians and instructors.
As social needs have changed, the purpose of the education program has gradually shifted away from supplying statistical technicians for the government, towards educating working people. Tutorial courses were therefore initiated.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation, andthe affiliated Statistical Technician Training Center was abolished. However, in response to consistent publicdemand for tutorial courses, the Center for Engineering and Technical Support, together with other departments,
ran three to four courses annually. In 2005, the number of courses rose to 13. Since 2012, the School of StatisticalThinking, which the Institute launched in 2011 in order to foster and promote statistical thinking, has been running
tutorial courses. In 2013, the number of courses was 15.

■ Courses
The total number of courses held from 1969 to March, 2014 was 308, with a total of 22,270 participants. These
courses covered a wide range of fields from basic to applied statistics. The following table lists the courses heldin the past year:
Number ofparticipantsYear

Category

Title

Month

2013

Standard course

Data Assimilation with the Ensemble Kalman Filter

April

47

Standard course

A Prospect of Earthquake Prediction Research

April

38

Basic course

Introduction to Sampling Methods and Sample Surveys

May

67

Standard course

Electro- Physical Modeling of Neural Systems and Its Mathematics of Dynamics

June

35

Basic course

Basic Course of Statistics

June

86

Basic course

Introduction to Dynamic Geometry Software “GeoGebra”
and Its Application to Mathematic Education

July

31

Basic course

Introduction to Multivariate Analysis

August94

2014

Standard course

Statistical Mathematics of Quality Control

September

35

Standard course

Special Lecture on Micro Marketing and Bayesian Modeling

October

60

Standard course

Discrete Optimization

November

68

Standard Advanced course

Introduction to Statistical Topic Models

December

96Standard course

New Developments on Omics Data Science

January

49

Standard course

Special Lecture on Micro Marketing and Bayesian Modeling

February69

Standard course

Robust Statistics

March

99

Standard Advanced course

Statistical Analysis of Random Partitions

March

33

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

The Statistical Research Division of the U.S. Bureau of the Census

Address
USA ( Washington)

Conclusion day

Stichting Mathematisch Centrum

The Kingdom of theNetherlands (Amsterdam)

May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin
Institute of Statistical Science, Academia Sinica
The Steklov Mathematical InstituteCentral South University
Soongsil University
Department of Statistics, University of Warwick
The Indian Statistical InstituteDepartment of Empirical Inference, Max Planck Institute for Biological Cybernetics
Faculty of Medicine, University of São Paulo
Deparztment of Communication Systems, SINTEF Information and Communication Technology
Human Language Technology Department Institute for Infocomm ResearchCentre for Computational Statistics and Machine Learning, University College London
Department of Electronics and Telecommunications, Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics, Charles University in Prague
The Department of Ecoinformatics, Biometrics and Forest Growth of the Georg-August University of Goettingen
The Korean Statistical Society
Toyota Technological Institute at Chicago

Germany (Berlin)
Taiwan ( Taipei )Russia (Moscow)
China (Changsha)

December 8, 2004
June 30, 2005
August 9, 2005
November 18, 2005
April 27, 2006
January 16, 2007
October 11, 2007
August 11, 2010
April 15, 2011
January 30, 2012
February 16, 2012
February 16, 2012
May 22, 2012
October 10, 2012
October 18, 2012
July 9, 2013
February 10, 2014

July 27, 1988

The Republic of Korea (Seoul )The United Kingdom (Coventry)
India (Kolkata )Germany ( Tubingen)
Brasil (São Paulo)
Norway ( Trondheim)
Singapore (Singapore)
The United Kingdom (London)
Norway (Trondheim)
Czech Republic (Prague)
Germany (Goettingen)
The Republic of Korea (Seoul )USA (Chicago)

Research Collaboration
This academic study program provides research­ers from other academic institutes with access to the facilitiesof the Institute, and provides opportunities for researchers to conduct theoretical and applied studies on statistics.

■ Number of Activities

2008

2009

2010

2011

2012

2013

138

154

135

172

182

181

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
Numberabc

FieldsNumberSpatial and Time Series
Modeling Group
Complex System Modeling Group
Latent Structure Modeling Group

d
e
f
g

FieldsNumberData Design Group
Metric Science Group
Structure Exploration Group
Mathematical Statistics GrouphijFieldsLearning and Inference Group
Computational Inference Group
OthersMajor Research Fields
Number18

FieldsMajor Research Domains

1

Statisticalmathematics

Mathematical theory of statistics,
optimization, etc.

6

Human
science

Philosophy, art, psychology, education,
history, geography, culture, language, etc.

2Information
science

Algorithms, use of computerin statistics, etc.

7Socialscience

Economics, law, politics, society, manage­
ment, official statistics, population, etc.

3

Biologicalscience

Medicine, pharmacy, epidemiology,
genetics, etc.

4PhysicalscienceSpace, planet, earth, polar region,materials, etc.

8

Environmental
science

Environmental Statistics, Environmetrics,
Agricultural Statistics, Land Economics,
Landscape Management, Forest
Management, etc.

5Engineering

Mechanics, electronics, control,chemistry, architecture, etc.

9

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education
and research program starting from the third year of study.

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order to extract information and knowledge from the real world based on empirical data, as well as basic,mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

19

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science byprofessors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of

the best in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible

for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the course are as follows:

Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research

results.

Number of Students ( As of April 1, 2014)■ Doctor’s course five years■ Doctor’s course three years

Year of enrollment

2009

2010

2012

2013

2014

Year of enrollment

2008

2010

2011

2012

2013

2014

Number of students

1 (1 )

2

2

2

1

Number of students

1

1

5

3

5

6  (1 )* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University (2) • Tohoku University ( 3 ) • Fukushima University (1 ) • University of Tsukuba (6) • Saitama University (1 )• Ochanomizu University (1 ) • Hitotsubashi University ( 6 ) • Chiba University (1 ) • The University of Tokyo (18 ) • Tokyo Medical and
Dental University (1 ) • Tokyo Gakugei University (2 ) • Tokyo Institute of Technology ( 4 ) • Tokyo University of Marine Science andTechnology (1 ) • Tokyo University of Agriculture and Technology (1 ) • Tokyo Metropolitan University (1 ) • Shizuoka University (1 )• Japan Advanced Institute of Science and Technology, Hokuriku (1 ) • Nagoya University ( 3 ) • Toyohashi University of Technology ( 2 )• Kyoto University ( 4 ) • Osaka University ( 2 ) • Osaka City University (1 ) • Nara Institute of Science and Technology (1 ) • Okayama
University ( 2 ) • Shimane University ( 3 ) • Kyushu University ( 2 ) • Oita University (1 )20Graduate School Program

University Background of Students
Private universities• Okayama University of Science (1 ) • Tokyo University of Science ( 6 ) • Kyoto Sangyo University (1 ) • Keio University ( 7) • Waseda
University ( 8 ) •Chuo University ( 7 ) • Toyo University (1 ) • Nihon University ( 2 ) • Hosei University ( 7 ) • Kurume University (1 )• Japan Women’s University (1 ) • Shibaura Institute of Technology (1 ) • Nanzan University (1 ) • Kansai University (1 ) • Kitasato
University (1 )Foreign universities• Aston University (1 ) • University of California, Irvine (1 ) • Universidade Eatadual of Campians (1 ) • University of Colorado at
Boulder ( 2 ) • University of Dhaka ( 2 ) • University of Hawaii (1 ) • Jahangirnagar University ( 2 ) • University of Malaya (1 ) • Northeast
Normal University (1 ) • Ohio University (1 ) • University of Rahshahi ( 2 ) • Stanford University (1 ) • Center for Analysis andPrediction, China Seismological Bureau (1 ) • Northeastern University of Technology, China (1 ) • The Hong Kong University of Scienceand Technology (1 ) • University of Science and Technology of China (1 ) • Chinese Academy of Sciences, Institute of Applied
Mathematics (1 )Degrees Awarded
Year

2008

2009

2010

Doctor of Philosophy

4 [1]

5 [1]

7 [1]

2011

2012

2013

4

6 [1]

6

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Science • Shibaura Institute ofTechnology • Rikkyo University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata

21

Facilities and Equipment

Computational Resources
The computing environment at the Institute of Statistical Mathematics has changed greatly in 2014. The Supercomputer System for Data Assimilation (nicknamed“A”) and the Communal Cloud Computing System (nicknamed “C”) were newly introduced on April 1. The Supercomputer System for Statistical Science (nicknamed“I”) was replaced, from the former Fujitsu system to an
SGI system, on July 1. The nicknames of these systems
come from the important statistical model selection criterion AIC invented by the former director general, Professor Hirotugu Akaike.
System “A” is the world’s largest shared-memory supercomputer system and consists of two SGI UV 2000
(256 10-core Xeon E5-4650v2, 64 TB memory). Half of thissystem is used as part of the high-performance computing infrastructure (HPCI) of Japan.
System “I” is a large distributed-memory supercomputer that consists of 400 SGI ICE X (two 12-core Xeon
E5-2697v2, 128 GB memory). The system also includes alarge-scale shared storage system (2.5 PB disk storage),physical random number generator boards, and a visualization system that supports a maximum resolution of

The Supercomputer System for Data Assimilation(nicknamed “A”)

4,096 × 2,160 and has a 200-inch rear projection screen
for 3D visualization.
System “C” consists of 64 Dell PowerEdge R620 (two10-core Xeon E5-2680v2, 256 GB memory). This systemprovides easy-to-use computing environments, such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software.
In the main office, the primary local area network(LAN) consists of an Ethernet network using 10GBASESR for the main trunk and 1000Base-T for branches. The
personal computers in researchers’ offices, and systems“A”, “I”, and “C” are all connected to this network. A wireless LAN system is also available in the immediate areaof the building occupied by the institute. These LAN systems enable distributed processing and allow computing resources and statistical data to be used effectively.
Comprehensive network security methods have been
implemented, such as a firewall system, anti-virus software, and an intrusion prevention system. To encourage
joint research with researchers both in Japan andabroad, as well as the exchange of e-mails, the network
is connected to the Internet through SINET4 (10 Gbps).

The Supercomputer System for Statistical
Science (nicknamed “I”)

The Communal Cloud Computing System(nicknamed “C”)

Library and Materials ( As of April 1, 2014)
We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

22

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

1,021
47%

Finance and Buildings

Administration Subsidy and Others ( 2013)
Type

Personnel expenses

Non-personnel expenses

Total

604,285

1,004,591

1,608,876

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2013)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

12

11

5

28

Income107,795

26,1707,800

141,765
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2013)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

2

10,010Grant-in-Aid for Scientific Research ( S )1

33,670Grant-in-Aid for Scientific Research ( A)3

36,390Grant-in-Aid for Scientific Research ( B)845,760Grant-in-Aid for Scientific Research ( C)18

25,090Grant-in-Aid for Challenging Exploratory Research

5

6,110

Grant-in-Aid for Young Scientists ( B )10

10,015Grant-in-Aid for Research Activity Start-up

3

3,770

Grant-in-Aid for JSPS Fellows

3

2,126

53

172,941

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2014)
Site Area

62,450m 2

Area for Buildings (total)

16,026m 2

23

Organization

Organization Diagram ( As of August 1, 2014 )24

Number of Staff ( As of April 1, 2014 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Professor Professor rative Staff
Staff

Total

1

1

Department of Statistical Modeling

6

6

2

14

Department of Data Science

5

8

4

17

Department of Mathematical Analysis and Statistical Inference

7

4

3

14

Center for Engineering and Technical Support

1010

Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

12 ( 28)

1 ( 2)

13 ( 30)

13 ( 28)

11 ( 2 )

70 ( 30)

Total

1

18

18
(

9

1

) Total number of staff of NIPR/ISM Joint Administration Office.

The number under Technical Staff at the Center for Engineering and Technical Support includes
one staff member who retired because of age but was reemployed in a different position.

Staff ( As of September 1, 2014 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Hiroshi MARUYAMA

Department of Statistical Modeling
Director Tomoko MATSUI• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————
Prof.

Nobuhisa KASHIWAGIProf.

Tomoyuki HIGUCHI

Assoc. Prof.

Genta UENO

Assist. Prof.

Shinya NAKANOAssoc. Prof.

Jiancang ZHUANG• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Shinsuke KOYAMA

Visiting Prof.

Takehiro FUKUIVisiting Prof.

Hiroko NAKANISHI

Visiting Assoc. Prof. Kazuhiro AOYAMA• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Hiroshi MARUYAMAProf.

Tomoko MATSUIAssoc. Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Kazuhiro MINAMI

Project Researcher

Akira TAMAMORI

Department of Data Science
Director Takashi NAKAMURA• Data Design Group ———————————————————————————————————————————————————————————————————
Prof.

Takashi NAKAMURA

Prof.

Ryozo YOSHINO

Assoc. Prof.

Naomasa MARUYAMA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Toshihiko KAWAMURA

25

Department of Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assist. Prof.

Nobuo SHIMIZU

Assist. Prof.

Hisashi NOMA

• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Hiroe TSUBAKI

Prof.

Koji KANEFUJI

Assoc. Prof.

Manabu KUROKI

Assist. Prof.

Yoo Sung PARK

Assoc. Prof.

Jun ADACHI

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Kei KOBAYASHI

Assist. Prof.

Teppei OGIHARA

Project Researcher

Jo NISHINO• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Project Assist. Prof. Osamu KOMORI

Hironori FUJISAWA

Visiting Prof.

Ryuei NISHII

Visiting Assoc. Prof. Yoshiyuki NINOMIYA• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Risk Analysis Research Center
Director Satoshi YAMASHITA

Hiroe TSUBAKI

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Nobuhisa KASHIWAGI

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Manabu KUROKI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Hisashi NOMA

Project Assist. Prof. Tomoaki IMOTO

26

Vice Director Masayuki HENMI

Prof.

Prof.

Project Assist. Prof. Buntaro KUSUMOTO

Project Assist. Prof. Yoshitake TAKEBAYASHI

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Toshiya SATO

Visiting Prof.

Yoichi KATO

Visiting Prof.

Masaaki MATSUURA

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Mihoko MINAMI

Visiting Prof.

Megu OTAKI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Osamu NAGAFUCHI

Visiting Prof.

Katsuhiko TAKATA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Tadashi ONO

Visiting Assoc. Prof. Hideki KATAGIRIVisiting Assoc. Prof. Koji OKUHARA

Visiting Assoc. Prof. Hisateru TACHIMORI

Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Toshikazu KITANO

Visiting Assoc. Prof. Hisayuki HARA

Organization

Staff
Risk Analysis Research Center
Visiting Assoc. Prof. Toshio ONISHI

Visiting Assoc. Prof. Toshihiro HORIGUCHI

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Katsuya TANAKA

Visiting Assoc. Prof. Masakazu ANDO

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Yasutaka SHIMIZU

Visiting Assoc. Prof. Masaaki FUKASAWA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Takaki IWATA

Project Researcher

Yuta KOIKE

Project Researcher

Takao KUMAZAWA

Project Researcher

Ryosuke NOMURA

Project Researcher

Min-zhen WANG

Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Prof.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assist. Prof.

Shinya NAKANO

Project Assist. Prof. Masaya SAITO

Project Assist. Prof. Terumasa TOKUNAGA

Visiting Prof.

Takashi WASHIO

Visiting Assoc. Prof. Kazuyuki NAKAMURA

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Osamu HIROSE

Project Researcher

Yuya ARIYOSHI

Visiting Assoc. Prof. Hiroshi KATO
Project Researcher

Kazue SUZUKI

Survey Science Center
Director Ryozo YOSHINOProf.

Ryozo YOSHINOProf.

Takashi NAKAMURA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Yoo Sung PARK

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Fumi HAYASHI

Visiting Prof.

Masato YONEDA

Visiting Prof.

Shintaro SONO

Visiting Prof.

Toru KIKKAWA

Visiting Prof.

Kazufumi MANABE

Visiting Prof.

Takatoshi IMADA

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Wataru MATSUMOTO

Visiting Assoc. Prof. Koken OZAKI

Visiting Assoc. Prof. Hiroko TSUNODA

Visiting Assoc. Prof. Tadayoshi FUSHIKI

Project Researcher

Project Researcher

Kiyohisa SHIBAI

Project Researcher

Yusuke INAGAKI

Kosuke NIKAIDO

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITO

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assist. Prof.

Kei KOBAYASHI

Visiting. Prof.

Atsuko IKEGAMI

Visiting. Prof.

Takashi TSUCHIYA

Visiting. Prof.

Masataka GOTO

Visiting. Prof.

Koji TSUDA

Project Assist. Prof. Yu NISHIYAMA
Visiting. Prof.

Tadashi WADAYAMA

Visiting Assoc. Prof. Yuji SHINANO

Visiting Assoc. Prof. Shaogao LU

Service Science Research Center
Director Hiroshi MARUYAMAProf.

Hiroshi MARUYAMAProf.

Tomoyuki HIGUCHIProf.

Hiroe TSUBAKI

Prof.

Tomoko MATSUIProf.

Junji NAKANOAssoc. Prof.

Manabu KUROKI

27

Service Science Research Center
Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Toshihiko KAWAMURA

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Yoshiki YAMAGATA

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Tadahiko SATO

Visiting Assoc. Prof. Haruhisa FUKUDA

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Yukihiko OKADA

School of Statistical Thinking
Director Junji NAKANO

Vice Director Yoshinori KAWASAKIProf.

Hiroshi MARUYAMAProf.

Satoshi ITO

Prof.

Yukito IBA

Assoc. Prof.

Naomasa MARUYAMA

Assist. Prof.

Teppei OGIHARA

Adjunct Professor

Yasumasa BABA

Adjunct Professor

Makio ISHIGURO

Adjunct Professor

Kunio SHIMIZU

Project Assist. Prof. Kei TAKAHASHI

Project Assist. Prof. Keiichi FUKAYA
Project Researcher

Project Assist. Prof. Kaname MATSUE

Project Assist. Prof. Toshiya KAZAMA

Naoki KAMIYA

Project Researchers
Project Assoc. Prof. Roberto Sebastian LEGASPIProject Researcher

Nana ARIZUMIProject Assist. Prof. Kazuhiko SHIBUYA
Project Researcher

Project Assist. Prof. Hei CHAN

Satoko SAITA

Project Researcher

Hisanao TAKAHASHI

Center for Engineering and Technical Support
Director

Junji NAKANO

Deputy Manager

Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Akiko NAGASHIMA

Vice Director
Senior Specialist

Yoshinori KAWASAKI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Junji NAKANO

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHI28

Head of Planning Unit

Hiroe TSUBAKI

Head of Evaluation Unit

Yoshiyasu TAMURA

Head of Information and Public Relations Unit

Hiroshi MARUYAMAHead of Intellectual Property Unit

Hiroshi MARUYAMAHead of NOE Promotion Unit

Hiroe TSUBAKI

Leader of URA Station

Kozo KITAMURA

Subleader of URA Station

Motoi OKAMOTO

Research Administrator

Keisuke HONDA

Research Administrator

Shigeru FUJITA

Research Administrator

Yoko OGAWA

Organization

Staff
NIPR/ISM Joint Administration OfficeDirector of NIPR/ISM Joint Administration Office

Tomoyoshi SHIBUSAWA

Director of General Service Center

Kazuhiko HASEGAWA• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Kenichi TADA
Deputy Head

Kazuhiko GOTO

Deputy Head

Yutaka ONO

Specialist

Fumio SUTOTeam Leader

Mitsuo ENDOTeam Leader

Hiroaki ARAI

Team Leader

Ichiro KAWAJI• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Michiaki NAKANO
Deputy Head

Koji SAKAMOTOTeam Leader

Kazuhisa OSHITATeam Leader

Yoji ISHIISpecialist

Hiroyasu KUMAGAITeam Leader

Norihito SETO

• General Service Center —————————————————————————————————————————————————————————————————————
Deputy Head

Motokazu TOYODA

Deputy Head

Takashi KUBOTA

Deputy HeadTomohiko MIYAUCHITeam Leader

Yumiko OKAWA

Team Leader

Susumu YAMAGUCHITeam Leader

Kenichi SHIOBARA

Specialist

Yoshihiro YAMADASpecialist

Michihito SAKURAI

Council of The Institute of Statistical Mathematics ( As of April 1, 2014)
Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering , Tokyo Institute of Technology

Masahiro MIZUTA

Professor, Information Initiative Center, and Graduate School of Information Science and Technology,
Hokkaido University

Shigeru OBAYASHI

Director Institute of Fluid Science Tohoku University

Nakahiro YOSHIDA

Professor , Graduate School of Mathematical Sciences, University of Tokyo

Nobuhiko TERUIProfessor, Graduate School of Economics and Management, Tohoku University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yoshihiro YAJIMA

Professor, Graduate School of Economics, The University of Tokyo

Shoichi YOKOYAMA

Professor, Department of Linguistic Theory and Structure, National Institute for Japanese Language and Linguistics

Masato OKADA

Professor, Graduate School of Frontier Sciences, The University of Tokyo

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Hiroe TSUBAKI

Professor (Vice Director-General, ISM )Yoshiyasu TAMURA

Professor ( Vice Director-General, ISM )Hiroshi MARUYAMA

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM )Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANOProfessor (Director of Center for Engineering and Technical Support, ISM )Nobuhisa KASHIWAGI

Professor (Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Satoshi ITOProfessor (Department of Mathematical Analysis and Statistical Inference, ISM )29

Organization

Cooperative Research Committee ( As of April 1, 2014 )Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Yoshinori FUJII

Professor, Faculty of Education and Culture, University of Miyazaki

Shigeyuki MATSUI

Professor, Graduate School of Medicine, Nagoya University

Hiroshi YADOHISA

Professor, Faculty of Culture and Information Science, Doshisha University

Kazue YAMAOKA

Professor, Graduate School of Public Health, Teikyo University

Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASHITA

Professor (Department of Data Science, ISM )Satoshi ITOProfessor (Department of Mathematical Analysis and Statistical Inference, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Research Ethics Review Committee ( As of April 1, 2014 )Specialist on epidemiology and social research

Kazuo SEIYAMA

Professor, Kwansei Gakuin University

Specialist on epidemiology and social research

Keiko SATO

Associate professor, Kyoto Unit Center Japan Environment &Children’s Study, Graduate School of Medicine, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director, Nishikokubunji Nursery School

Research education staff of ISM

Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM)

Research education staff of ISM

Hiroe TSUBAKI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM)

Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis andStatistical Inference, ISM )Professor Emeritus ( As of April 1, 2014 )30

Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social science
theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as an Inter-University Research Institute due to the regulation change. The new mission includes
providing facilities and skills to other universities, in addition to conducting cutting-edge research on statisticalmathematics. Accordingly, the institute was reorganized into our basic research departments (Fundamental
Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) and two strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The Statistical
Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation
Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI,
was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Sciences were
instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2008

April

The Research Innovation Center was instituted.
The Intellectual Property Unit was instituted.

2009

October

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within the Administration Planning and Coordination Section.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

February

A new team, the University Research Administrators (URAs), is formed and made a subsection of the Administration Planning and Coordination Section.

31

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/